After exploring the general pattern of modelling GPP vs observational GPP, the next step to identify the specific period when the mismatch between modeled GPP and observed GPP in each site–>focused in the markdown file
step1: tidy the table for GPP simulation vs GPP obs sites
step2: finding the way to separate out the model early simulation period
library(kableExtra)
library("readxl")
table.path<-"D:/CES/Data_for_use/Merge_Data/ECflux_and_PhenoCam_site_info/"
# my_data <- read.csv(paste0(table.path,"ECflux_and_PhenoCam_site_info_add_manually.csv"))
#after updating the information, now new updated information is:
my_data <- read.csv(paste0(table.path,"ECflux_and_PhenoCam_site_info_add_manually_final.csv"))
# my_data %>%
# kbl(caption = "Summary of sites with early GPP estimation") %>%
# kable_paper(full_width = F, html_font = "Cambria") %>%
# scroll_box(width = "500px", height = "200px") #with a scroll bars
my_data %>%
kbl(caption = "Summary of sites with GPP estimation beyond Beni's datasets") %>%
kable_classic(full_width = F, html_font = "Cambria")
| SiteName | Site_years | Site_fullme | Lat. | Long. | ELV. | PFT | MAT | MAP | Clim. | Site_flag | Delay_status | Period | N | Calib. | Avai.alyzed.years.spring | Avai.site.years.spring | Avai.alyzed.years.springawinter | Avai.site.years.springawinter | Reference | PhenoCam | Cam_Period | Cam_Source | Cam_sitename | Cam_Avai |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AT-Neu | 10.926027 | Neustift | 47.1167 | 11.3175 | 970 | GRA | 6.50 | 852.00 | Dfc | Beyond | 2002-2012 | NA | 2002-2012 | NA | 2002-2012 | NA | Y | 2012-2021 | PhenoCam USA | innsbruck | Y | |||
| BE-Bra | 14.175342 | Brasschaat | 51.3076 | 4.5198 | 16 | MF | 9.80 | 750.00 | Cfb | Beyond | no obs data | NA | no obs data | NA | no obs data | NA | Y | 2009-2014 | EuroPhen | Brasschaat | N | |||
| CA-NS1 | 3.131507 | UCI-1850 burn site | 55.8792 | -98.4839 | 260 | ENF | -2.89 | 500.29 | Dfc | Beyond | 2002-2005 | NA | 2003-2005 | NA | 2003-2004 | NA | N | |||||||
| CA-NS3 | 4.142466 | UCI-1964 burn site | 55.9117 | -98.3822 | 260 | ENF | -2.87 | 502.22 | Dfc | Beyond | 2001-2005 | NA | 2002-2005 | NA | 2002-2004 | NA | N | |||||||
| CH-Fru | 8.520548 | Frebel | 47.1158 | 8.5378 | 982 | GRA | 7.20 | 1651.00 | Dfb | Beyond | 2005-2014 | NA | 2006-2014 | NA | 2007-2008, 2010-2014 | NA | Y | 2008-2014 | EuroPhen | Früebüel | N | |||
| CN-Qia | 3.000000 | Qianyanzhou | 26.7414 | 115.0581 | NA | ENF | 18.95 | 1466.75 | Cfa | Beyond | 2003-2006 | NA | 2003-2006 | NA | 2003-2006 | NA | N | |||||||
| CZ-BK1 | 10.167123 | Bily Kriz forest | 49.5021 | 18.5369 | 875 | ENF | 6.70 | 1316.00 | Dfb | Beyond | 2004-2008 | NA | 2004-2008 | NA | 2004-2008 | NA | N | |||||||
| CZ-BK2 | 5.816438 | Bily Kriz grassland | 49.4944 | 18.5429 | 855 | GRA | 6.70 | 1316.00 | Dfb | Beyond | 2006-2007 | NA | lack early doy | NA | no years | NA | N | |||||||
| DE-Lkb | 3.967123 | Lackenberg | 49.0996 | 13.3047 | 1308 | ENF | 4.00 | 1599.00 | Dfc | Beyond | 2009-2013 | NA | 2010-2013 | NA | 2010-2013 | NA | N | |||||||
| DK-Sor | 17.742466 | Soroe | 55.4859 | 11.6446 | 40 | DBF | 8.20 | 660.00 | Dfb | Beyond | 2000-2014 | NA | 2000-2003,2005-2013 | NA | 2000-2003,2005-2013 | NA | Y | 2009-2014 | EuroPhen | Sorø | Y | |||
| FR-Fon | 9.424658 | Fontainebleau-Barbeau | 48.4764 | 2.7801 | 103 | DBF | 10.20 | 720.00 | Cfb | Beyond | 2005-2014 | NA | 2006-2013 | NA | 2006-2013 | NA | Y | 2012-2014 | EuroPhen | Fontainebleau | N | |||
| FR-LBr | 10.509589 | Le Bray | 44.7171 | -0.7693 | 61 | ENF | 13.60 | 900.00 | Cfb | Beyond | 2000-2008 | NA | 2001, 2004-2008 | NA | 2001, 2004-2008 | NA | N | |||||||
| IT-Col | 13.315069 | Collelongo | 41.8494 | 13.5881 | 1560 | DBF | 6.30 | 1180.00 | Dfb | Beyond | 2000-2014 | NA | 2001,2005,2007-2011,2013-2014 | NA | 2001,2005,2007-2011,2013-2014 | NA | N | |||||||
| IT-Lav | 11.679452 | Lavarone | 45.9562 | 11.2813 | 1353 | ENF | 7.80 | 1291.00 | Dfb | Beyond | 2003-2014 | NA | 2003-2014 | NA | 2003-2014 | NA | N | |||||||
| IT-MBo | 10.854795 | Monte Bondone | 46.0147 | 11.0458 | 1550 | GRA | 5.10 | 1214.00 | Dfb | Beyond | 2003-2013 | NA | 2003-2013 | NA | 2003-2013 | NA | Y | 2015-2021 | PhenoCam USA | montebondonegrass | Y | |||
| JP-SMF | 3.920548 | Seto Mixed Forest Site | 35.2617 | 137.0788 | NA | MF | NA | NA | Cfa | Beyond | 2002-2006 | NA | 2003-2006 | NA | 2003,2005-2007 | NA | N | |||||||
| RU-Fyo | 15.465753 | Fyodorovskoye | 56.4615 | 32.9221 | 265 | ENF | 3.90 | 711.00 | Dfb | Beyond | 2000-2014 | NA | 2000-2001,2003-2014 | NA | 2000-2001,2003-2014 | NA | N | |||||||
| US-AR1 | 3.386301 | ARM USDA UNL OSU Woodward Switchgrass 1 | 36.4267 | -99.4200 | 611 | GRA | NA | NA | Cfa | Beyond | 2009-2012 | NA | bad quality | NA | NA | N | ||||||||
| US-AR2 | 3.106849 | ARM USDA UNL OSU Woodward Switchgrass 2 | 36.6358 | -99.5975 | 646 | GRA | NA | NA | Cfa | Beyond | 2009-2012 | NA | bad quality | NA | NA | N | ||||||||
| US-GLE | 9.293151 | GLEES | 41.3665 | -106.2399 | 3197 | ENF | 0.80 | 1200.00 | Dfc | Beyond | 2004-2014 | NA | 2006-2014 | NA | 2006-2014 | NA | N | |||||||
| US-Ha1 | 20.115068 | Harvard Forest EMS Tower (HFR1) | 42.5378 | -72.1715 | 340 | DBF | 6.62 | 1071.00 | Dfb | Beyond | 2000-2012 | NA | 2000-2012 | NA | 2000-2012 | NA | Y | 2015-2021 | PhenoCam USA | bbc1-bbc2 | Y | |||
| US-MMS | 15.619178 | Morgan Monroe State Forest | 39.3232 | -86.4131 | 275 | DBF | 10.85 | 1032.00 | Dfa | Beyond | 2000-2014 | NA | 2000-2014 | NA | 2000-2014 | NA | Y | 2008-2021 | PhenoCam USA | morganmonroe | Y | |||
| US-NR1 | 15.627397 | Niwot Ridge Forest (LTER NWT1) | 40.0329 | -105.5464 | 3050 | ENF | 1.50 | 800.00 | Dfc | Beyond | 2000-2014 | NA | 2000-2014 | NA | 2000-2014 | NA | Y | 2009-2015 | PhenoCam USA | niwot2 | Y | |||
| US-PFa | 17.704110 | Park Falls/WLEF | 45.9459 | -90.2723 | 470 | MF | 4.33 | 823.00 | Dfb | Beyond | 2000-2014 | NA | 2000-2004,2006-2014 | NA | NA | N | ||||||||
| US-Prr | 3.852055 | Poker Flat Research Range Black Spruce Forest | 65.1237 | -147.4876 | 210 | ENF | -2.00 | 275.00 | Dfc | Beyond | 2010-2013 | NA | 2010-2012 | NA | 2011 | NA | N | |||||||
| DE-Hai | NA | 51.0800 | 10.4500 | NA | DBF | NA | NA | Cfb | Beni | Yes | 2000-2012 | 4247 | Y | 2000-2012 | 13 | 2000-2012 | 13 | Knohl et al. (2003) | Y | 2003-2014 | EuroPhen | Y | ||
| US-Syv | NA | 46.2400 | -89.3500 | NA | MF | NA | NA | Dfb | Beni | Yes | 2001-2014 | 2635 | Y | 2002-2006, 2014 | 6 | 2002, 2004-2006,2014 | 5 | Desai et al. (2005) | Y | 2015-2021 | PhenoCam USA | sylvania | Y | |
| US-UMB | NA | 45.5600 | -84.7100 | NA | DBF | NA | NA | Dfb | Beni | Yes | 2000-2014 | 4015 | Y | 2000-2014 | 15 | 2000-2014 | 15 | Gough et al. (2013) | Y | 2008-2021 | PhenoCam USA | umichbiological | Y | |
| US-UMd | NA | 45.5600 | -84.7000 | NA | DBF | NA | NA | Dfb | Beni | Yes | 2007-2014 | 2050 | Y | 2008-2014 | 7 | 2008-2013 | 6 | Gough et al. (2013) | Y | 2008-2021 | PhenoCam USA | umichbiological2 | Y | |
| US-WCr | NA | 45.8100 | -90.0800 | NA | DBF | NA | NA | Dfb | Beni | Yes | 1999-2014 | 3425 | Y | 2000-2006, 2011-2014 | 11 | 2000-2006, 2011-2014 | 11 | Cook et al. (2004) | Y | 2011-2021 | PhenoCam USA | willowcreek | Y | |
| CA-Man | NA | 55.8800 | -98.4800 | NA | ENF | NA | NA | Dfc | Beni | Yes | 1994-2008 | 1910 | 2000-2003, 2007-2008 | 6 | 2000-2003, 2007 | 5 | Dunn et al. (2007) | N | ||||||
| CA-NS2 | NA | 55.9100 | -98.5200 | NA | ENF | NA | NA | Dfc | Beni | Yes | 2001-2005 | 1123 | 2002, 2004 (2003 lack early doy) | 2 | 2002 | 1 | N | |||||||
| CA-NS4 | NA | 55.9100 | -98.3800 | NA | ENF | NA | NA | Dfc | Beni | Yes | 2002-2005 | 756 | 2005 (2003 lack early doy) | 1 | no years | 0 | N | |||||||
| CA-NS5 | NA | 55.8600 | -98.4800 | NA | ENF | NA | NA | Dfc | Beni | Yes | 2001-2005 | 1245 | 2002, 2004-2005 (2003 lack early doy) | 3 | 2002, 2004 | 2 | N | |||||||
| CA-Qfo | NA | 49.6900 | -74.3400 | NA | ENF | NA | NA | Dfc | Beni | Yes | 2003-2010 | 2416 | 2004-2010 | 7 | 2004-2010 | 7 | Bergeron et al. (2007) | Y | 2008-2011 | PhenoCam USA | chibougamau | Y | ||
| FI-Hyy | NA | 61.8500 | 24.3000 | NA | ENF | NA | NA | Dfc | Beni | Yes | 1996-2014 | 4587 | Y | 2000-2014 | 15 | 2000-2004, 2006-2014 | 14 | Suni et al. (2003) | Y | 2008-2014 | EuroPhen | Y | ||
| IT-Tor | NA | 45.8400 | 7.5800 | NA | GRA | NA | NA | Dfc | Beni | Yes | 2008-2014 | 2172 | Y | 2009-2014 | 6 | 2009-2014 | 6 | Galvagno et al. (2013) | Y | 2009-2021 | PhenoCam USA | torgnon-nd | Y | |
| IT-Ren | NA | 46.5900 | 11.4300 | NA | ENF | NA | NA | Dfc | Beni | No | 1998-2013 | 3405 | Y | 2002-2003,2005-2013 | 11 | 2002-2003,2005-2013 | 11 | Montagni et al. (2009) | N | |||||
| BE-Vie | NA | 50.3100 | 6.0000 | NA | MF | NA | NA | Cfb | Beni | No | 1996-2014 | 4910 | Y | 2000-2014 | 15 | 2000-2014 | 15 | Aubinet et al. (2001) | Y | 2010-2014 | EuroPhen | Y | ||
| CH-Cha | NA | 47.2100 | 8.4100 | NA | GRA | NA | NA | Cfb | Beni | No | 2005-2014 | 2944 | 2006-2008,2010-2014 | 8 | 2006-2008,2010-2014 | 8 | Merbold et al. (2014) | N | ||||||
| CH-Lae | NA | 47.4800 | 8.3700 | NA | MF | NA | NA | Cfb | Beni | No | 2004-2014 | 3551 | Y | 2005-2014(2004 lack early doy) | 10 | 2005-2014(2004 lack early doy) | 10 | Etzold et al. (2011) | Y | 2010-2014 | EuroPhen | Y | ||
| CH-Oe1 | NA | 47.2900 | 7.7300 | NA | GRA | NA | NA | Cfb | Beni | No | 2002-2008 | 2184 | Y | 2002-2008 | 7 | 2002-2008 | 7 | Ammann et al. (2009) | N | |||||
| DE-Gri | NA | 50.9500 | 13.5100 | NA | GRA | NA | NA | Cfb | Beni | No | 2004-2014 | 3642 | Y | 2004-2014 | 11 | 2004-2014 | 11 | Prescher et al. (2010) | Y | 2007-2014 | EuroPhen | N | ||
| DE-Obe | NA | 50.7800 | 13.7200 | NA | ENF | NA | NA | Cfb | Beni | No | 2008-2014 | 2260 | Y | 2008-2014 | 7 | 2008-2014 | 7 | N | ||||||
| DE-RuR | NA | 50.6200 | 6.3000 | NA | GRA | NA | NA | Cfb | Beni | No | 2011-2014 | 1227 | Y | 2012-2014 | 3 | 2012-2014 | 3 | Post et al. (2015) | N | |||||
| DE-Tha | NA | 50.9600 | 13.5700 | NA | ENF | NA | NA | Cfb | Beni | No | 1996-2014 | 5141 | Y | 2000-2014 | 15 | 2000-2014 | 15 | Grünwald and Bernhofer (2007) | Y | 2009-2014 | EuroPhen | Y | ||
| NL-Hor | NA | 52.2400 | 5.0700 | NA | GRA | NA | NA | Cfb | Beni | No | 2004-2011 | 2188 | Y | 2005,2007-2011 | 6 | 2005,2007-2010 | 5 | Jacobs et al. (2007) | N | |||||
| NL-Loo | NA | 52.1700 | 5.7400 | NA | ENF | NA | NA | Cfb | Beni | No | 1996-2013 | 4671 | Y | 2000-2013 | 14 | 2000-2013 | 14 | Moors (2012) | N |
## [1] 11
(1) For Cfa:both for MF and ENF sites - Cfa-MF (1 site)
## [1] 4
- Cfa-ENF (1 site)
## [1] 3
(2) For Cfb: for DBF and ENF - Cfb-DBF (1 site)
## [1] 8
## [1] 6
(3) For Dfa: for DBF - Dfa-DBF (1 site)
## [1] 15
(3) For Dfb: for GRA, DBF, MF and ENF - Dfb-GRA (2 sites)
## [1] 9
## [1] 11
## [1] 13
## [1] 9
## [1] 13
- Dfb-MF (1 sites)
## [1] 14
- Dfb-ENF (3 sites)
## [1] 5
## [1] 12
## [1] 14
(4) For Dfc:both for GRA and ENF sites
## [1] 11
## [1] 3
## [1] 4
## [1] 4
## [1] 9
## [1] 15
## [1] 3
##working to here–>08-26 ## step3: save the data that label with “is_event”
Step1: normlization for all the years in one site
#normalized the gpp_obs and gpp_mod using the gpp_max(95 percentile of gpp)
Step 2:Determine the green-up period for each year(using spline smoothed values):
#followed analysis is based on the normlized “GPP_mod”time series(determine earlier sos)
using the normalized GPP_mod to determine sos,eos and peak of the time series (using the threshold, percentile 10 of amplitude, to determine the sos and eos in this study). We selected the GPP_mod to determine the phenophases as genearlly we can get earlier sos compared to GPP_obs–> we can have larger analysis period
Step 3:rolling mean of GPPobs and GPPmod for data for all the years(moving windown:5,7,10, 15, 20days)
also for the data beyond green-up period–> the code of this steps moves to second step
Step 4:Fit the Guassian norm distribution for residuals beyond the green-up period
The reason to conduct this are: we assume in general the P-model assume the GPP well outside the green-up period (compared to the observation data).
But in practise, the model performance is not always good beyond the green-up period–>I tested three data range:
[peak,265/366]
DoY[1, sos]& DOY[peak,365/366]
[1,sos] & [eos,365/366]
I found the using the data range c, the distrbution of biase (GPP_mod - GPP_obs) is more close to the norm distribution, hence at end of I used the data range c to build the distribution.
step 5:determine the “is_event” within green-up period
After some time of consideration, I took following crition to determine the “is_event”:
during the green-up period (sos,peak)–>the data with GPP biases bigger than 3 SD are classified as the “GPP overestimation points”
For “GPP overestimation points” –> only regard the data points in the first 2/3 green-up period as the “is_event”
For “is_event points”, thoses are air temparture is less than 10 degrees will be classified as the “is_event_less10”. I selected 10 degree as the crition by referring to the paper Duffy et al., 2021 and many papers which demonstrate the temperature response curve normally from 10 degree (for instance: Lin et al., 2012)
References:
Duffy et al., 2021:https://advances.sciencemag.org/content/7/3/eaay1052
Lin et al., 2012:https://academic.oup.com/treephys/article/32/2/219/1657108
step 6:Evaluation “is_event”–>visualization and stats
visulization
stats: \[ Pfalse = /frac{days(real_{(is-event)})}{days(flagged_{(is-event)})} \]